William D Beavis | bioRxiv Science

bioRxiv2016

Numericware i: Identical in state matrix calculator

Herein we introduce software, Numericware i to compute a matrix consisting of all pairwise identical in state (IIS) coefficients from genotypic data. Since the emergence of high throughput technology for genotyping, calculating an IIS matrix between many pairs of entities has required large computer memory and lengthy processing times. Numericware i addresses these limitations with two algorithmic methods: multithreading and forward chopping. The multithreading feature allows computational routines to concurrently run on multiple CPU processors. The forward chopping addresses memory limitations by dividing the genotypic data into appropriately sized subsets. Numericware i allows researchers who need to estimate an IIS matrix for big genotypes to use typical laptop/desktop computers. For comparison with different software, we calculated genetic relationship matrices using Numericware i, SPAGeDi and TASSEL with the same small-sized data set. Numericware i measured kinship coefficients between zero and two, while the matrices from SPAGeDi and TASSEL produced different ranges of values, including negative values. The Pearson correlation coefficient between the matrices from Numericware i and TASSEL was high at 0.993, while SPAGeDi rarely showed correlation with Numericware i (0.088) and TASSEL (0.087). To compare the capacity with high dimensional data, we applied the three software to a simulated data set consisted of 500 entities by 1,000,000 SNPs. Numericware i spent 71 minutes using seven CPU cores on a laptop (DELL LATITUDE E6540), while SPAGeDi and TASSEL failed to start. Numericware i is freely available for Windows and Linux under CC-BY license at https://figshare.com/s/f100f33a8857131eb2db.

Bioinformatics↗

bioRxiv2016

Evaluation of a Computational Diagnostic for Epistasis in Plant Breeding Populations

Previously we reported the inability of genomic prediction methods based on linear models to accurately predict trait values composed of an epistatic genetic architecture. We also reported non-parametric genomic prediction methods applied to the same data produced reasonably accurate predictions. The difference led us to propose analyses by paired parametric and non-parametric methods to the same data could be used as a diagnostic for epistatic genetic architectures in typical plant breeding populations. The suggested computational diagnostic was based on evaluation of 14 genomic prediction methods applied to eight sets of simulated conditions consisting of three factors, each with two levels. Because the potential set of factors that might affect accuracies of genomic predictions is unknown, there is a need for a systematic approach to identify combinations of factors that impact estimates of accuracy. Herein we propose the application of response surface methods to systematically identify conditions that maximize the difference between estimated accuracies of genomic prediction methods. The results indicate that genetic architecture and repeatability at their upper boundaries for complete epistasis and repeatability have the greatest influence on the differences between parametric and non-parametric estimated prediction accuracies. Further, the surface is very steep in the vicinity of the boundary conditions, indicating that the proposed diagnostic is of limited value for discovery of epistatic genetic architectures.

Genomics↗